With explosion of 3D geometric data and its increasing usage in many applications, ranging
from computer-aided design to medicine to paleontology, shape analysis is becoming an
important research field. Common to many shape analysis tasks are two sub-problems: 1)
segmenting a shape into meaningful parts and 2) identifying important/salient points on
a shape. Both are easier for humans than for computers. In this thesis, we revisit these
problems, from the angle of using crowdsourced data to learn from humans.
We first investigate the problem of mesh segmentation. By constructing a benchmark of
4300 manually generated segmentations for 380 surface meshes of 19 different object categories,
and developing software to analyze 11 geometric properties of segmentations and
to compute 4 quantitative metrics for comparison of segmentations, we are able to quantitatively
answer "How do people decompose shapes into meaningful parts" and "How
algorithms do in comparison with humans". The benchmark is widely adopted for evaluating
new segmentation algorithms and prompts emergence of learning-based algorithms for
mesh segmentation and labeling.
We then visit the problem of identifying salient points on meshes. Rather than defining
saliency bottom-up from low-level geometric features, we start with the social/psychological
essence of saliency by investigating Schelling points [1] on 3D meshes. We designed an
online pure coordination game that asked people to select points on 3D surfaces that they
expect will be selected by other people. We then analyzed properties of the selected points,
finding that Schelling point sets are usually highly symmetric, that local curvature properties
proposed in previous work are most helpful for identifying obvious Schelling points
while global properties (e.g., segment centeredness, proximity to symmetry axis, etc.) are
required to explain more subtle features. Based on these observations, we use regression to
combine multiple properties into an analytical model that predicts where Schelling points
are likely to be on new meshes.
Shared by these two problems are the desire to transfer properties and distributions collected
for known meshes to unseen ones. Finally, we propose MeshMatch, a mesh-based
analogy of the image-based PatchMatch [2] algorithm and a non-parametric multi-resolution
approach to surface property transfer based on this algorithm. This method offers benefits
of both parametrization and texture synthesis approaches to preserving both large- and finescale
patterns of the source properties. We show its applications in texture transfer, detail
transfer, texture painting, and transferring Schelling distributions.